CC BY
7*
ALT'23 The 30th International Conference on Advanced Laser Technologies
LM-I-19
Machine learning and multiparametric investigation of laser ablation in liquid for the synthesis of nanoparticles
Vincenzo Amendola
University of Padova, Department of Chemical Sciences, Via Marzolo 1, 35131 Padova - ITALY Main author email address: vincenzo.amendola@unipd.it
In recent years, laser ablation in liquid (LAL) made a whole library of nanomaterials available for integration in green emerging technologies.[1-3] However, the achievement of a specific type of nanocrystals by LAL has been until now a rather empirical endeavour based on changing synthesis parameters and characterizing the products. Here we started from the bibliographic analysis on LAL of Cu-based nanocrystals, to identify the relevant synthesis features and lead to the predetermination of the optimal conditions for producing Cu-based nanoparticles with defined copper oxidation state. First, single features and their combinations were screened by linear regression analysis to find the best correlation with experimental output and identify an equation for predicting LAL results. Then, machine learning algorithms were exploited to unravel cross correlations between features which are hidden to the linear regression analysis. This approach is of general applicability to any other nanomaterial and can help understanding the origin of the chemical pathways of nanomaterials generated by LAL, ultimately providing a rational guideline for the conscious predetermination of synthetic parameters toward the desired compounds.
[1] V. Amendola et al., Room-Temperature Laser Synthesis in Liquid of Oxide, Metal-Oxide Core-Shells, and Doped Oxide Nanoparticles, Chem. Eur. J., 26, 9206 - 9242 (2020).
[2] E.V. Barmina et al.; Laser ablation and fragmentation of Boron in liquids, Optics Laser Tech. 155, 108393 (2022).
[3] A.A. Popov, Z. Swiatkowska-Warkocka, S.M. Klimentov,T.E. Itina, A.V. Kabashin et al.; Laser-Ablative Synthesis of Ultrapure Magneto-Plasmonic Core-Satellite Nanocomposites for Biomedical Applications, Nanomaterials, 12, 649 (2022).
